Farid Karimipour

Voronoi-Based Approaches for Geosensor Networks Coverage Determination and Optimisation: A Survey

Recent advances in sensors technology have led to design and development of variety of geosensor networks and their applications in many disciplines. Such networks consist of a set of sensors laying on different locations and sensing various real world phenomena for environmental monitoring, object surveillance, tracking and controlling applications. A fundamental issue in a geosensor network optimization is estimation of its spatial coverage. The existence of various obstacles in the sensing environment and its complexity result in several holes in the environment. These holes should be detected and minimized in the optimization process. Different approaches have been proposed in the literatures to resolve these problems. A considerable number of current approaches use Voronoi diagram and Delaunay triangulation to identify the holes in the network and deploy an optimal arrangement for the sensors. These structures are more compatible with the spatial distribution of sensors in the environment. This paper presents a survey of the existing solutions for geosensor network optimization that use Voronoi diagram and Delaunay triangulation in their approach.

A GIS based wireless sensor network coverage estimation and optimization: a voronoi approach

Recent advances in sensor technology have resulted in the design and development of more efficient and low cast sensor networks for environmental monitoring, object surveillance, tracking and controlling of moving objects, etc. The deployment of a sensor network in a real environment presents several challenging issues that are often oversimplified in the existing solutions. Different approaches have been proposed in the literatures to solve this problem. Many of these approaches use Voronoi diagram and Delaunay triangulation to identify sensing holes in the network and create an optimal arrangement of the sensors to eliminate the holes. However, most of these methods do not consider the reality of the environment in which the sensor network is deployed. This paper presents a survey of the existing solutions for geosensor network optimization that use Voronoi diagram and Delaunay triangulation and identifies their limitations in a real world application. Next, it proposes a more realistic approach by integrating spatial information in the optimization process based on Voronoi diagram. Finally the results of two cases studies based on the proposed approach in natural area and urban environment are presented and discussed.

A Stable Voronoi-based Algorithm for Medial Axis Extraction through Labeling Sample Points

This paper presents a Voronoi-based algorithm to extract the medial axis through labeling sample points. A major issue of the medial axis is its inherent instability under small perturbations. The medial axis is very sensitive to small changes of the boundary, which produce many irrelevant branches in the medial axis. Filtering extraneous branches is a common solution to handle this issue, It may be applied as a pre-processing step through simplifying (smoothing) the boundary, or as a post-processing step through pruning, which eliminates the irrelevant branches of the extracted medial axis. However, filtering may alter the topological or geometrical structure of the medial axis. This paper proposes a modification to a Voronoi-based medial axis extraction algorithm to automatically avoid appearing irrelevant branches through labeling the sample points. The experimental results indicate that our method is stable, even in the presence of significant noises and perturbations.

Citizens as Expert Sensors: One Step Up on the VGI Ladder

Volunteered geographic information is one the most significant advances in GIScience following the well-known elements of Web 2.0. It has been mainly looked at as a source of information, and the focus has been on improving its usage, understanding, and quality. This paper opens a discussion on considering VGI as an implicit source of users’ experience, which provides general users with solutions to help them take actions like an expert. As a case study, the idea has been applied on estimating the optimum travel time path through information collected by experts. Having introduced the methodology, the results for some examples are presented and discussed.

Watershed delineation from the medial axis of river networks

The watersheds are commonly delineated from digital elevation models (DEM). This approach is not efficient when an accurate DEM is not available. Furthermore, since raster-based algorithms are employed, the computations for large areas are very time consuming and even may be impractical. This article investigates delineation of the watersheds from the medial axis of river networks: If the river network is sampled by a set of points, the medial axis of the sample points provides an approximation of the catchments, whose aggregation results in the watersheds. Although this idea has been already proposed in the literature, the complexities of the medial axis extraction prevent it from being practically used. A major issue is appearing extraneous branches in the media axis due to perturbations of the sample points, which must be filtered out in a pre- or post-processing step. This article improves a Voronoi-based medial axis extraction algorithm by using labeled sample points to automatically avoid extraneous branches. The proposed method is used in four case studies to delineate the watersheds. The results illustrate that the proposed method is stable, easy to implement and robust, even in presence of significant noises and perturbations. The results also indicate that the watersheds delineated using the proposed and the DEM-based methods are reasonably comparable.

Voronoi-Based curve reconstruction: issues and solutions

Continuous curves are approximated by sampling. If sampling is sufficiently dense, the sample points carry the shape information of the curve and so can be used to reconstruct the original curve. There have been lots of efforts to reconstruct curves from sample points. This paper reviews the curve reconstruction methods that use Voronoi diagram in their approach. We, then, describe the main issues of these methods and suggest solutions to deal with them. Especially, we improve one of the Voronoi-based curve reconstruction algorithms (called one-step crust algorithm) by labeling the sample points as a pre-processing. The highlights of our proposed approach are (1) It is simple and easy to implement; (2) It is robust to boundary perturbations and noises; (3) Special cases in sampling like sharp corners can be handled; and (4) It can be used for reconstructing open curves.

Voronoi-Based Medial Axis Approximation from Samples: Issues and Solutions

Continuous curves are approximated by sample points, which carry the shape information of the curve. If sampling is sufficiently dense, the sample points can be used to extract the structural properties of the curve (e.g., crust, medial axis, etc.). This article focuses on approximation of medial axis from sample points. Especially, we review the methods that approximate the medial axis using Voronoi diagram. Such methods are extremely sensitive to noise and boundary perturbations. Thus, a pre- or post-processing step is needed to filter irrelevant branches of the medial axis, which are introduced in this article. We, then, propose a new medial axis approximation algorithm that automatically avoids irrelevant branches through labeling sample points. The results indicate that our method is stable, easy to implement, robust and able to handle sharp corners, even in the presence of significant noise and perturbations.

Contextual Adaptability of Navigational Spatial Descriptions: A Pragmatic Comparison

Different forms of spatial descriptions are used to communicate information in the context of navigation in urban environments. When generated by computers, such descriptions are combinations of map features in a predefined way. Unlike computers, however, people are capable of flexibly generating navigational spatial descriptions by taking into account a wide array of different contextual factors, e.g. a user’s prior knowledge and the structure of the environment. This paper deploys the notion of pragmatics to compare formal addresses, route descriptions (generated either by computers or humans), and destination descriptions in terms of their adaptability to contextual factors in order to identify the means to creating more cognitively sound information systems.

Fuzzy spatial association rule mining to analyze the effect of environmental variables on the risk of allergic asthma prevalence

AbstractThe prevalence of allergic diseases has greatly increased in recent decades, likely due to contamination of the environment with allergy irritants. One common treatment is identifying that allergy irritant, and then avoiding exposure to it. This article studies the relation between the prevalence of allergic asthma and certain allergy irritants that are related to environmental variables. To that end, we use spatial association rule mining to determine the association between the spatial distribution of allergic asthma prevalence and air pollutants such as CO, SO2, NO2, PM10, PM2.5, and O3 (from data compiled by air pollution monitoring stations), as well as other factors, such as the distance of residence from parks and roads. In order to clear up the uncertainties inherent in the attributes linked to the spatial data, the dimensions in question have been defined as fuzzy sets. Results for the case study (i.e. Tehran metropolitan area) indicate that distance to parks and roads, as well as CO, NO2...

On Correlation Between Demographic Variables and Movement Behavior

The importance of studying the behavior of people and the expansion of access to spatial data has led to development of activities related to study of movement of individuals as well as discovery of patterns and behavior of individuals for better use in urban planning and policymaking. Understanding the relationship between demographic variables and human movement as well as extraction of behavioral patterns is essential to assess different social issues such as locating infrastructures and city management, reducing traffic and structure of urban communities. This paper aims to explore a Swiss human movement sample dataset, called MDC, in order to discover the effect of demographic parameters on human movement patterns in Switzerland. The users’ movement is characterized by area and shape index of the movements as the determinants of the activity space. The results declare that middle age users, females and people who work have more active mobility pattern, since they have higher area and shape index than users in other groups. Data analysis and comparison of results indicate that age and working are two decisive demographic factors for area and shape index of the activity space so they are useful for understanding some of the human’s movement characteristics.